Note: Code sections can be made visible by using the provided buttons throughout this project

!¤%& 1.0 ABSTRACT

Topic

Problem formulation

Research question

Concepts

Dataset and main data analytics methods and tools

Most important results

Conclusions and recommendations

2.0 INTRODUCTION

Since 2008, Airbnb has grown from a small accommodation platform, hosted in San Francisco, to one that is now recognised throughout the world. Airbnb has revolutionized the tourism housing industry by applying a sharing economy model to the accommodation business. Today, Airbnb has become the world’s largest accommodation service provider with more accommodation options than any other accommodation business - and even more than all of them combined. As a platform, Airbnb enables people (hosts) to offer accommodation services to other people (guests), providing guests with a more unique and personalized way of experiencing the world, and often at a reasonably lower price than other accommodation options. Only just a fraction (20%) of these transactions are captured by Airbnb, which in 2019 returned 4,7 billion USD in sales revenues.

2.1 PROBLEM FORMULATION AND RESEARCH QUESTION

Data plays a key role in Airbnb’s success. For instance, data enables Airbnb to match guests and hosts and further allows the users to filter the host listings to their likings, in respect of pricing, location, number of beds, and much more. Thereby, data is essential to securing high customer satisfaction. Moreover, Airbnb can use the collected data to extract insights that can be used to improve their service offerings, guide decision making, guide marketing initiatives, and more.

As a platform, Airbnb’s sole value creation lies in creating successful matches between guests and hosts and by ensuring a positive experience for both parties. Naturally, if the platform fails to deliver a positive experience to a user, the user might neglect the platform in total, resulting in negative feedback loops. This leads us to our research question:

How can Airbnb ensure matches and the experiences they create are positive for their customers (users), and providers (hosts)? Moreover, how can Airbnb help guide user decisions to create successful matches and positive experiences?

Currently, Airbnb helps the users to create meaningful matches, by allowing the guests to limit their search for accommodation by different attributes related to the individual host listing. As such, users can easily find accommodation that meets their basic needs for accommodation; e.g. number of beds, bedrooms, price, room type, etc. However, without any knowledge of the different location areas, guests might find difficulty in choosing a location that suits their needs.

In this project, we will examine the accommodation services, listed on Airbnb for Copenhagen, in special regard to the location areas, and the attributes that are associated with them. The goal is to create a report that can guide customers to choose a location that lives up to their expectations, thereby improving the quality of the matches provided by the platform.

3.0 METHODOLOGY

3.1 DATASET DESCRIPTION

The data was downloaded from the independent site: Inside Airbnb, which scrapes data from Airbnb, and makes it puplicly available for analysis. This site provides a multitude of datasets containing information on the most populated cities around the world - including Copenhagen.

The datasets provided by Inside Airbnb is as follows: (1) listings, (2) calendar, (3) reviews, (4) listings_summary, (5) reviews_summary.

We have downloaded and inspected all of the datasets, however the calendar dataset is assessed to be unimportant. Thus, the listings and reviews data has been chosen to conduct this project. Furthermore, we chose the most recently scraped data: 28th of Nov. 2020.

3.1.1 LISTINGS.CSV.GZ

The listings dataset contains data about the airbnb host listings and their respective attributes. In total, there are 74 columns describing 8636 listings on the Airbnb platform.

Here, we provide you with a short glimpse of some of the attributes in the listings dataset:

id: primary key (listings_id)
name: name of the listing
neightbourhood_cleansed: location area
latitude: latitude
longitude: longitude
beds: number of beds in the room
bedrooms: number of bedrooms in the room
price: price of the room per day
room_type: type of room that is made available
property_type: type of the property where the room is in
review_score: average review score of the listing

3.1.2 REVIEWS.CSV.GZ

The reviews dataset contains data about reviews that were given for the listed accommodation services. In total, there are 6 columns describing 185.564 reviews.

Here, we provide you with a short glimpse of some of the attributes in the reviews dataset:

listing_id: foreign key references listings
id: primary key (review_id)
date: date review was written
reviewer_name: name of reviewer
comments: review text

3.2 Dataset Analysis Process

For this project we perform an exploratory data analysis of the data, as well as visualize the processed data in an interactive map, and furthermore display wordclouds of the review text.

Before we can start the analysis we install the necessary libraries for our python interpreter to work with the data. Specifically, we will be using pandas to create and manage the data in pandas dataframe. We use Plotly's Express library to visualize the data in an interactive map. Furthermore, we use WordCloud and Matplotlib's Pyplot to visualize wordsclouds of the review texts.

Once the necessary packages are installed and imported, we can begin to gather the data and initiate the data cleaning process. We use pandas to download the data from Inside Airbnb and to uncompress it, using the in-built decompressor.

We then give a quick glimpse of the datasets that we gathered, before beginning the data cleaning. To clean the data, start by removing columns that are empty, listings that has never been reviewed, renaming columns to be easier interpretable, and finally correcting the data types.

We should now have clean data, that we can use to analyze the attributes of the listings. Here, we will investigate the distributions of prices, neighbourhoods, property types, and room types. Similarly, we will investigate the average prices of listings by neighbourhood, property type, and by room type. In conclusion, we aim to list differnces that occour for each different category.

Nearing the end, the two datasets are merged into one dataframe, that contains data about both listings and reviews. These are joined by the listings_id using the 'inner' property. We can then use this dataframe to display the interactive map, and wordclouds that summarized the reviews of each neighbourhood.

Now, let's get started!

Install Libraries

import pandas as pd
import requests
import matplotlib.pyplot as plt
import plotly.express as px
import numpy as np

from os import path
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator

Gathering the Data

#Create DataFrames
listings = pd.read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-11-28/data/listings.csv.gz', compression='gzip')
reviews = pd.read_csv('http://data.insideairbnb.com/denmark/hovedstaden/copenhagen/2020-11-28/data/reviews.csv.gz', compression='gzip')

Quick Glimpse of the Data

Listings

listings.head()

listings.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8636 entries, 0 to 8635
Data columns (total 74 columns):
 #   Column                                        Non-Null Count  Dtype  
---  ------                                        --------------  -----  
 0   id                                            8636 non-null   int64  
 1   listing_url                                   8636 non-null   object 
 2   scrape_id                                     8636 non-null   int64  
 3   last_scraped                                  8636 non-null   object 
 4   name                                          8635 non-null   object 
 5   description                                   8396 non-null   object 
 6   neighborhood_overview                         4936 non-null   object 
 7   picture_url                                   8636 non-null   object 
 8   host_id                                       8636 non-null   int64  
 9   host_url                                      8636 non-null   object 
 10  host_name                                     8634 non-null   object 
 11  host_since                                    8634 non-null   object 
 12  host_location                                 8614 non-null   object 
 13  host_about                                    5045 non-null   object 
 14  host_response_time                            5180 non-null   object 
 15  host_response_rate                            5180 non-null   object 
 16  host_acceptance_rate                          6708 non-null   object 
 17  host_is_superhost                             8634 non-null   object 
 18  host_thumbnail_url                            8634 non-null   object 
 19  host_picture_url                              8634 non-null   object 
 20  host_neighbourhood                            5662 non-null   object 
 21  host_listings_count                           8634 non-null   float64
 22  host_total_listings_count                     8634 non-null   float64
 23  host_verifications                            8636 non-null   object 
 24  host_has_profile_pic                          8634 non-null   object 
 25  host_identity_verified                        8634 non-null   object 
 26  neighbourhood                                 4936 non-null   object 
 27  neighbourhood_cleansed                        8636 non-null   object 
 28  neighbourhood_group_cleansed                  0 non-null      float64
 29  latitude                                      8636 non-null   float64
 30  longitude                                     8636 non-null   float64
 31  property_type                                 8636 non-null   object 
 32  room_type                                     8636 non-null   object 
 33  accommodates                                  8636 non-null   int64  
 34  bathrooms                                     0 non-null      float64
 35  bathrooms_text                                8623 non-null   object 
 36  bedrooms                                      8440 non-null   float64
 37  beds                                          8584 non-null   float64
 38  amenities                                     8636 non-null   object 
 39  price                                         8636 non-null   object 
 40  minimum_nights                                8636 non-null   int64  
 41  maximum_nights                                8636 non-null   int64  
 42  minimum_minimum_nights                        8636 non-null   int64  
 43  maximum_minimum_nights                        8636 non-null   int64  
 44  minimum_maximum_nights                        8636 non-null   int64  
 45  maximum_maximum_nights                        8636 non-null   int64  
 46  minimum_nights_avg_ntm                        8636 non-null   float64
 47  maximum_nights_avg_ntm                        8636 non-null   float64
 48  calendar_updated                              0 non-null      float64
 49  has_availability                              8636 non-null   object 
 50  availability_30                               8636 non-null   int64  
 51  availability_60                               8636 non-null   int64  
 52  availability_90                               8636 non-null   int64  
 53  availability_365                              8636 non-null   int64  
 54  calendar_last_scraped                         8636 non-null   object 
 55  number_of_reviews                             8636 non-null   int64  
 56  number_of_reviews_ltm                         8636 non-null   int64  
 57  number_of_reviews_l30d                        8636 non-null   int64  
 58  first_review                                  7337 non-null   object 
 59  last_review                                   7337 non-null   object 
 60  review_scores_rating                          7230 non-null   float64
 61  review_scores_accuracy                        7228 non-null   float64
 62  review_scores_cleanliness                     7228 non-null   float64
 63  review_scores_checkin                         7229 non-null   float64
 64  review_scores_communication                   7229 non-null   float64
 65  review_scores_location                        7228 non-null   float64
 66  review_scores_value                           7228 non-null   float64
 67  license                                       0 non-null      float64
 68  instant_bookable                              8636 non-null   object 
 69  calculated_host_listings_count                8636 non-null   int64  
 70  calculated_host_listings_count_entire_homes   8636 non-null   int64  
 71  calculated_host_listings_count_private_rooms  8636 non-null   int64  
 72  calculated_host_listings_count_shared_rooms   8636 non-null   int64  
 73  reviews_per_month                             7337 non-null   float64
dtypes: float64(20), int64(21), object(33)
memory usage: 4.9+ MB

listings.shape

(8636, 74)

Reviews

reviews.head(20)

reviews.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 185564 entries, 0 to 185563
Data columns (total 6 columns):
 #   Column         Non-Null Count   Dtype 
---  ------         --------------   ----- 
 0   listing_id     185564 non-null  int64 
 1   id             185564 non-null  int64 
 2   date           185564 non-null  object
 3   reviewer_id    185564 non-null  int64 
 4   reviewer_name  185564 non-null  object
 5   comments       185464 non-null  object
dtypes: int64(3), object(3)
memory usage: 8.5+ MB

reviews.shape

(185564, 6)

Preprocessing

Cleaning Listing Data

#Removing empty columns
listings.drop(columns=['neighbourhood_group_cleansed','bathrooms','calendar_updated','license'], inplace=True)

#Deselect Listings with no reviews
listings = listings[listings.number_of_reviews > 0]

#Rename columns
listings.rename(columns={'id':'listing_id','name':'listing_name','description':'listing_description'},inplace=True)

#Correct DataTypes
listings = listings.astype(
    {
     #DateTime:
     'last_scraped':'datetime64[ns]',
     'host_since':'datetime64[ns]',
     'calendar_last_scraped':'datetime64[ns]',
     'first_review':'datetime64[ns]',
     'last_review':'datetime64[ns]'
     }
    )

#Correct Prices from $ to DKK, then DataType
listings.price = listings.price.str.replace(',','')
listings.price = listings.price.str.replace('$','')
listings.price = listings.price.astype(float)

Cleaning Reviews Data

#Rename Columns
reviews.rename(columns={'id':'reivew_id','date':'review_date','comments':'review_text'},inplace=True)

#Change DataTypes
reviews.review_date = pd.to_datetime(reviews.review_date)

EDA

Distributions

Neighbourhoods

neighbourhoods = listings['neighbourhood_cleansed'].value_counts().to_frame(name='listings').reset_index()
neighbourhoods

Property Types

listings[['property_type']].value_counts().to_frame(name='listings').reset_index()

Room Types

listings[['room_type']].value_counts().to_frame(name='listings').reset_index()

Property Types by Neighbourhood

#Select properties listed more than 400 times
listings_clean = listings[listings.property_type.isin(['Entire apartment','Private room in apartment','Entire condominium','Entire house'])]

#Count number of listings in neighbourhoods by property type   
listings_byNeighbourhood = listings_clean.groupby(['neighbourhood_cleansed','property_type']).neighbourhood_cleansed.count().to_frame(name = 'listings').reset_index()

#Sum number of listings per neighbourhood
listingsNeighbourhoodCount = listings_byNeighbourhood.groupby('neighbourhood_cleansed')['listings'].sum().to_frame(name = 'total_listings').sort_values(by='total_listings', ascending=False).reset_index()

#Calculate ratio of property types in the different neighbourhoods
neighbourhoodPropertyRatio = listings_byNeighbourhood.merge(listingsNeighbourhoodCount, on='neighbourhood_cleansed')
neighbourhoodPropertyRatio['ratio_of_property_type_in_neighbourhood'] = neighbourhoodPropertyRatio['listings']/neighbourhoodPropertyRatio['total_listings']*100
neighbourhoodPropertyRatio.head(10)

Room Types by Neighbourhood

#Count number of listings in neighbourhoods by property type   
roomCount = listings.groupby(['neighbourhood_cleansed','room_type']).neighbourhood_cleansed.count().to_frame(name = 'listings').reset_index()

#Sum number of listings per neighbourhood
roomNeighbourhoodCount = roomCount.groupby('neighbourhood_cleansed')['listings'].sum().to_frame(name = 'total_listings').sort_values(by='total_listings', ascending=False).reset_index()

#Calculate ratio of property types in the different neighbourhoods
roomRatio = roomCount.merge(listingsNeighbourhoodCount, on='neighbourhood_cleansed')
roomRatio['ratio_of_room_type_in_neighbourhood'] = roomRatio['listings']/roomRatio['total_listings']*100
roomRatio.head(10)

Accomodations by Neighbourhood

df = pd.DataFrame()
df['avg_n_accommodations'] =listings.groupby('neighbourhood_cleansed').accommodates.mean()
df = df.reset_index()
df

Pricing by Neighbourhood

neighbourhoodPricing = listings.groupby('neighbourhood_cleansed').price.mean().to_frame().sort_values(by='price', ascending=False).reset_index()
neighbourhoodPricing

#Calculate Average Price Per Person
df['price'] = neighbourhoodPricing.price
df['price_perPerson'] = df.price/df.avg_n_accommodations
df

Pricing by Property Type

PropertyPricing = listings.groupby('property_type').price.mean().to_frame().sort_values(by='price', ascending=False).reset_index()
PropertyPricing.head(20)

Pricing by Room Type

roomPricing = listings.groupby('room_type').price.mean().to_frame().sort_values(by='price', ascending=False).reset_index()
roomPricing.head(20)

Visualizations

Maps

#Merge reviews and listings
group_listingReviews = reviews.merge(listings, on='listing_id', how='inner')

#Define mapbox API token and style
mapbox_access_token = 'pk.eyJ1IjoiYWNodG9uMjExMSIsImEiOiJja2lyam5yemgyNTV0MnJsYmJ0NXdzNWRxIn0.rWJgur27hJnWoBt7Oq5LeQ'
px.set_mapbox_access_token(mapbox_access_token)
plot_style = 'mapbox://styles/achton2111/ckirsv5df0aj01at4zp0d7f3w'

#Interactive Geospacial plot
fig = px.scatter_mapbox(group_listingReviews,
                        lat="latitude",
                        lon="longitude",
                        color="neighbourhood_cleansed",
                        zoom=10,
                        size='price',
                        mapbox_style= plot_style,
                        hover_name='listing_name',
                        hover_data = {'price',
                                      'property_type',
                                      'room_type',
                                      'accommodates',
                                      'beds',
                                      'review_scores_rating'},
                        opacity = 0.8,
                        title = 'AirBnB Listing Locations. Coloured by Neighbourhood, Size by Price)'
                        )
fig.show()

WordClouds

Let's try to see if there are any visual differences between reviews in the different neighbourhoods

#Distinction between neighbourhoods
norrebro = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Nrrebro']
indreby = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Indre By']
vesterbro_KgsEnghave = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Vesterbro-Kongens Enghave']
osterbro = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'sterbro']
frederiksberg = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Frederiksberg']
amagerOst = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Amager st']
amagerVest = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Amager Vest']
valby = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Valby']
bispebjerg = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Bispebjerg']
vanlose = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Vanlse']
bronshojHusum = group_listingReviews[group_listingReviews.neighbourhood_cleansed == 'Brnshj-Husum']

Norrebro

# Iterating through the .csv data file 
for i in norrebro.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Indre By

# Iterating through the .csv data file 
for i in indreby.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Vesterbro - Kongens Enghave

# Iterating through the .csv data file 
for i in vesterbro_KgsEnghave.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Østerbro

# Iterating through the .csv data file 
for i in osterbro.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Frederiksberg

# Iterating through the .csv data file 
for i in frederiksberg.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Amager Øst

# Iterating through the .csv data file 
for i in amagerOst.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Amager Vest

# Iterating through the .csv data file 
for i in amagerVest.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Valby

# Iterating through the .csv data file 
for i in valby.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Bispebjerg

# Iterating through the .csv data file 
for i in bispebjerg.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Vanløse

# Iterating through the .csv data file 
for i in vanlose.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

Brønshøj - Husum

# Iterating through the .csv data file 
for i in bronshojHusum.review_text: 
    i = str(i) 
    separate = i.split() 
    for j in range(len(separate)): 
        separate[j] = separate[j].lower() 
    comment_words += " ".join(separate)+" "

# Creating the Word Cloud
final_wordcloud = WordCloud(width = 3000, height = 2000, 
                background_color ='black', 
                stopwords = STOPWORDS, 
                min_font_size = 10).generate(comment_words)

# Displaying the WordCloud                    
plt.figure(facecolor = None) 
plt.imshow(final_wordcloud) 
plt.axis("off") 
plt.tight_layout(pad = 0) 
  
plt.show()

	id	listing_url	scrape_id	last_scraped	name	description	neighborhood_overview	picture_url	host_id	host_url	host_name	host_since	host_location	host_about	host_response_time	host_response_rate	host_acceptance_rate	host_is_superhost	host_thumbnail_url	host_picture_url	host_neighbourhood	host_listings_count	host_total_listings_count	host_verifications	host_has_profile_pic	host_identity_verified	neighbourhood	neighbourhood_cleansed	neighbourhood_group_cleansed	latitude	longitude	property_type	room_type	accommodates	bathrooms	bathrooms_text	bedrooms	beds	amenities	price	minimum_nights	maximum_nights	minimum_minimum_nights	maximum_minimum_nights	minimum_maximum_nights	maximum_maximum_nights	minimum_nights_avg_ntm	maximum_nights_avg_ntm	calendar_updated	has_availability	availability_30	availability_60	availability_90	availability_365	calendar_last_scraped	number_of_reviews	number_of_reviews_ltm	first_review	last_review	review_scores_rating	review_scores_accuracy	review_scores_cleanliness	review_scores_checkin	review_scores_communication	review_scores_location	review_scores_value	license	instant_bookable	calculated_host_listings_count	calculated_host_listings_count_entire_homes	calculated_host_listings_count_private_rooms	reviews_per_month
0	6983	https://www.airbnb.com/rooms/6983	20201128003729	2020-11-28	Copenhagen 'N Livin'	Lovely apartment located in the hip Nørrebro a...	Nice bars and cozy cafes just minutes away, ye...	https://a0.muscache.com/pictures/42044170/f63c...	16774	https://www.airbnb.com/users/show/16774	Simon	2009-05-12	Copenhagen, Capital Region of Denmark, Denmark	I'm currently working as an environmental cons...	NaN	NaN	0%	f	https://a0.muscache.com/im/users/16774/profile...	https://a0.muscache.com/im/users/16774/profile...	Nørrebro	1.0	1.0	['email', 'phone', 'reviews']	t	f	Copenhagen, Hovedstaden, Denmark	Nrrebro	NaN	55.68798	12.54571	Private room in apartment	Private room	2	NaN	1 shared bath	1.0	1.0	["Hot water", "Refrigerator", "Heating", "Stov...	$361.00	2	15	2	2	15	15	2.0	15.0	NaN	t	29	59	89	89	2020-11-28	168	0	2009-09-04	2019-07-19	96.0	10.0	9.0	10.0	10.0	9.0	9.0	NaN	f	1	0	1	1.23
1	26057	https://www.airbnb.com/rooms/26057	20201128003729	2020-11-29	Lovely house - most attractive area	Our lovely house in the center of the city is ...	The neighborhood is the most famous one and th...	https://a0.muscache.com/pictures/bfbca07e-4cc1...	109777	https://www.airbnb.com/users/show/109777	Kari	2010-04-17	Copenhagen, Capital Region of Denmark, Denmark	We are a family with 2 children, and living in...	within a few hours	100%	0%	f	https://a0.muscache.com/im/users/109777/profil...	https://a0.muscache.com/im/users/109777/profil...	Indre By	1.0	1.0	['email', 'phone', 'reviews', 'jumio', 'offlin...	t	t	Copenhagen, Hovedstaden, Denmark	Indre By	NaN	55.69163	12.57459	Entire house	Entire home/apt	6	NaN	1.5 baths	4.0	4.0	["Kitchen", "Essentials", "Cooking basics", "I...	$2,400.00	3	30	3	3	30	30	3.0	30.0	NaN	t	11	30	60	335	2020-11-29	50	1	2013-12-02	2019-12-14	98.0	10.0	10.0	10.0	10.0	10.0	10.0	NaN	f	1	1	0	0.59
2	29118	https://www.airbnb.com/rooms/29118	20201128003729	2020-11-28	Best Location in Cool Istedgade	<b>The space</b><br />The apartment is situate...	NaN	https://a0.muscache.com/pictures/236213/33956c...	125230	https://www.airbnb.com/users/show/125230	Nana	2010-05-15	Copenhagen, Capital Region of Denmark, Denmark	I have a Master of Arts in Musicology and I wo...	NaN	NaN	25%	f	https://a0.muscache.com/im/users/125230/profil...	https://a0.muscache.com/im/users/125230/profil...	Vesterbro	1.0	1.0	['email', 'phone', 'reviews', 'offline_governm...	t	t	NaN	Vesterbro-Kongens Enghave	NaN	55.67069	12.55430	Entire apartment	Entire home/apt	2	NaN	1 bath	1.0	1.0	["Hot water", "Changing table", "Refrigerator"...	$725.00	7	14	3	5	14	14	4.1	14.0	NaN	t	0	23	53	143	2020-11-28	22	0	2010-06-17	2019-08-02	98.0	10.0	10.0	10.0	10.0	10.0	10.0	NaN	f	1	1	0	0.17
3	31094	https://www.airbnb.com/rooms/31094	20201128003729	2020-11-28	Very central and cozy, new kitchen	<b>The space</b><br />Welcome to our home, we ...	What else is nearby?<br />To be honest, I thin...	https://a0.muscache.com/pictures/19408864/7c4c...	129976	https://www.airbnb.com/users/show/129976	Ebbe	2010-05-22	Copenhagen, Capital Region of Denmark, Denmark	Hi and welcome. My name is Ebbe, I am a medica...	NaN	NaN	NaN	f	https://a0.muscache.com/im/users/129976/profil...	https://a0.muscache.com/im/users/129976/profil...	Vesterbro	1.0	1.0	['email', 'phone', 'facebook', 'reviews', 'jum...	t	t	Copenhagen, Capital Region of Denmark, Denmark	Vesterbro-Kongens Enghave	NaN	55.66744	12.55516	Entire apartment	Entire home/apt	3	NaN	1 bath	1.0	3.0	["TV", "Heating", "Kitchen", "Wifi", "Cable TV"]	$750.00	2	10	2	2	10	10	2.0	10.0	NaN	t	0	0	0	211	2020-11-28	17	0	2010-08-16	2017-08-25	97.0	10.0	10.0	10.0	10.0	10.0	9.0	NaN	f	1	1	0	0.14
4	32841	https://www.airbnb.com/rooms/32841	20201128003729	2020-11-28	Cozy flat for Adults/Quiet for kids	Our flat is placed in a Central AND Quiet neig...	NaN	https://a0.muscache.com/pictures/28047599/8efd...	142143	https://www.airbnb.com/users/show/142143	Anders & Maria	2010-06-10	Copenhagen, Capital Region of Denmark, Denmark	Anders:\r\nHitchhiked 100.000 km's, Been publi...	within a few hours	100%	0%	f	https://a0.muscache.com/im/pictures/user/88f79...	https://a0.muscache.com/im/pictures/user/88f79...	Østerbro	1.0	1.0	['email', 'phone', 'facebook', 'reviews', 'jum...	t	t	NaN	sterbro	NaN	55.71158	12.56879	Entire apartment	Entire home/apt	4	NaN	1 bath	2.0	2.0	["TV", "Free parking on premises", "Iron", "He...	$618.00	100	1125	100	100	1125	1125	100.0	1125.0	NaN	t	0	2	32	307	2020-11-28	7	0	2010-07-25	2016-09-15	91.0	10.0	9.0	10.0	10.0	9.0	9.0	NaN	f	1	1	0	0.06

	listing_id	id	date	reviewer_id	reviewer_name	comments
0	6983	8985	2009-09-04	31133	Jeff	Convenient location, clean apartment, great br...
1	6983	9199	2009-09-07	11472	Andrea	We really enjoyed staying to Simon in our firs...
2	6983	11763	2009-09-28	35317	Jeanine	Friendly host, save neighbourhood, clean appar...
3	6983	20616	2009-12-17	53151	Sonja	Simon is great! Fantastic place to stay, supe...
4	6983	23168	2010-01-04	50856	James	Great bed, clean apartment, comfy bed, accomod...
5	6983	33706	2010-04-05	96881	Tom	Great value, very clean and comfortable apartm...
6	6983	40860	2010-05-08	113545	Orion	Amazingly helpful and friendly, close to town,...
7	6983	51622	2010-06-10	135677	Gonzalo	Thoroughly recommended, Simon is a great host ...
8	6983	54476	2010-06-18	138676	Tiffany	Fantastic host! Simon was friendly, outgoing a...
9	6983	56504	2010-06-23	141069	Payton	Great location in an interesting neighborhood ...
10	6983	1073551	2012-04-02	366694	Ruifeng	Simon is an extremely nice host. The apartment...
11	6983	1133218	2012-04-15	977908	Johnier Alberto	Very clean, comfortable, and convenient Plus, ...
12	6983	1159109	2012-04-21	1960762	Luca	Nice flat in Central Area
13	6983	1179503	2012-04-25	2062676	Bojana	It was really nice staying in Simon's flat, I ...
14	6983	1334891	2012-05-23	1747677	Jeanne	Simon was a great host - the flat was in a gre...
15	6983	1355404	2012-05-27	1852940	Stephan	Very nice apartment, kitchen fully equipped an...
16	6983	1615224	2012-07-02	2673311	Sarah	Simon was a great host. Even though he was awa...
17	6983	2867447	2012-11-12	3617497	Nadja	my boyfriend and I spent one weekend at simons...
18	6983	2897562	2012-11-16	4042783	Edith	Simon was a very sweet and laid back host, the...
19	6983	2909008	2012-11-18	4042783	Edith	Simon was a very sweet and laid back host, the...

	neighbourhood_cleansed	room_type	listings	total_listings	ratio_of_room_type_in_neighbourhood
0	Amager Vest	Entire home/apt	529	551	96.007260
1	Amager Vest	Hotel room	1	551	0.181488
2	Amager Vest	Private room	108	551	19.600726
3	Amager Vest	Shared room	2	551	0.362976
4	Amager st	Entire home/apt	434	466	93.133047
5	Amager st	Hotel room	3	466	0.643777
6	Amager st	Private room	108	466	23.175966
7	Amager st	Shared room	1	466	0.214592
8	Bispebjerg	Entire home/apt	206	246	83.739837
9	Bispebjerg	Private room	55	246	22.357724

	neighbourhood_cleansed	avg_n_accommodations	price	price_ratio	price_perPerson
0	Amager Vest	3.668750	1532.850631	417.812779	417.812779
1	Amager st	3.483516	1039.018018	298.267002	298.267002
2	Bispebjerg	3.249042	1020.058608	313.956718	313.956718
3	Brnshj-Husum	4.589552	1017.957228	221.798811	221.798811
4	Frederiksberg	3.470012	1002.586291	288.928748	288.928748
5	Indre By	3.816971	956.114063	250.490291	250.490291
6	Nrrebro	3.102770	874.478999	281.838134	281.838134
7	Valby	3.550562	809.671642	228.040431	228.040431
8	Vanlse	3.872928	753.269663	194.496161	194.496161
9	Vesterbro-Kongens Enghave	3.230967	752.756906	232.981949	232.981949
10	sterbro	3.464607	674.777778	194.763125	194.763125

	property_type	price
0	Boat	1839.714286
1	Entire villa	1800.774194
2	Houseboat	1636.083333
3	Island	1600.000000
4	Private room	1500.000000
5	Entire loft	1488.738636
6	Entire townhouse	1476.600000
7	Entire serviced apartment	1459.496732
8	Entire house	1371.062130
9	Room in serviced apartment	1235.400000
10	Entire condominium	1161.533981
11	Private room in tiny house	1123.500000
12	Entire apartment	1104.796330
13	Entire cabin	1022.000000
14	Entire bungalow	1009.666667
15	Room in hotel	921.333333
16	Room in hostel	894.529412
17	Room in boutique hotel	872.666667
18	Hut	850.000000
19	Entire guest suite	786.222222

	index	listings
0	Indre By	1426
1	Vesterbro-Kongens Enghave	1169
2	Nrrebro	1119
3	Frederiksberg	817
4	sterbro	777
5	Amager Vest	640
6	Amager st	546
7	Valby	267
8	Bispebjerg	261
9	Vanlse	181
10	Brnshj-Husum	134

	property_type	listings
0	Entire apartment	4905
1	Private room in apartment	916
2	Entire condominium	412
3	Entire house	338
4	Entire townhouse	155
5	Entire serviced apartment	153
6	Private room in house	89
7	Entire loft	88
8	Private room in condominium	54
9	Private room in townhouse	39
10	Entire villa	31
11	Private room in villa	21
12	Room in hostel	17
13	Houseboat	12
14	Private room in bed and breakfast	11
15	Entire guesthouse	9
16	Entire guest suite	9
17	Private room in hostel	8
18	Shared room in apartment	8
19	Private room in loft	7
20	Boat	7
21	Private room in guesthouse	6
22	Private room in guest suite	6
23	Room in serviced apartment	5
24	Tiny house	4
25	Shared room in hostel	4
26	Private room in bungalow	3
27	Room in boutique hotel	3
28	Entire cabin	3
29	Entire bungalow	3
30	Room in hotel	3
31	Private room in tiny house	2
32	Private room in boat	2
33	Hut	1
34	Island	1
35	Private room	1
36	Private room in serviced apartment	1

	room_type	listings
0	Entire home/apt	6131
1	Private room	1169
2	Hotel room	25
3	Shared room	12

	neighbourhood_cleansed	property_type	listings	total_listings	ratio_of_property_type_in_neighbourhood
0	Amager Vest	Entire apartment	373	551	67.695100
1	Amager Vest	Entire condominium	37	551	6.715064
2	Amager Vest	Entire house	62	551	11.252269
3	Amager Vest	Private room in apartment	79	551	14.337568
4	Amager st	Entire apartment	301	466	64.592275
5	Amager st	Entire condominium	36	466	7.725322
6	Amager st	Entire house	59	466	12.660944
7	Amager st	Private room in apartment	70	466	15.021459
8	Bispebjerg	Entire apartment	168	246	68.292683
9	Bispebjerg	Entire condominium	18	246	7.317073

	room_type	price
0	Entire home/apt	1151.074539
1	Hotel room	960.080000
2	Shared room	618.583333
3	Private room	584.256630